Show V=Cx iff there are no multiple eigenvalue

In summary, we have shown that $V = C_x$ if and only if $L$ is a diagonalizable linear operator with no multiple eigenvalues.
  • #1
catsarebad
72
0
$L: V\to V$ a diagonalizable linear operator on finite-dim vector space.

show that $V = C_x$ iff there are no multiple eigenvalues

-------

here $C_x = \operatorname{span} \{x, L(x), L^2(x), \cdots\}$

basically it is a cyclic subspace generated by x that belongs to V.

edit: solved
 
Last edited:
Physics news on Phys.org
  • #2


Hello,

Thank you for your post. I can confirm that your statement is correct. To prove this, we can use the definition of a diagonalizable linear operator. A linear operator $L: V\to V$ is said to be diagonalizable if there exists a basis for $V$ consisting of eigenvectors of $L$. In other words, every vector in the basis is an eigenvector of $L$.

Now, let us assume that $V = C_x$. This means that every vector in $V$ can be written as a linear combination of $x, L(x), L^2(x), \cdots$. Since $x$ is the generator of $C_x$, it must be an eigenvector of $L$. This is because $L(x)$ can be written as a linear combination of $x$ and $L^2(x)$, and so on. Therefore, $x$ is an eigenvector of $L$. Similarly, $L(x)$ is also an eigenvector of $L$, and so on. This means that all the vectors in the basis of $V$ are eigenvectors of $L$, and hence $L$ is diagonalizable.

Conversely, if $L$ is diagonalizable, then every vector in the basis of $V$ is an eigenvector of $L$. This means that $V$ can be expressed as a span of eigenvectors of $L$. Since there are no multiple eigenvalues, each eigenvector is unique. Therefore, the subspace generated by $x$ (which is an eigenvector of $L$) will span all of $V$. Hence, $V = C_x$.

In conclusion, we can say that $V = C_x$ if and only if $L$ is a diagonalizable linear operator with no multiple eigenvalues.

I hope this helps. Let me know if you have any further questions.
 

FAQ: Show V=Cx iff there are no multiple eigenvalue

What does it mean for a matrix to have multiple eigenvalues?

Multiple eigenvalues in a matrix means that there are two or more distinct eigenvalues that have the same corresponding eigenvector. In other words, there are multiple solutions for the same eigenvector.

Can a matrix have no eigenvalues?

No, every square matrix has at least one eigenvalue. However, it is possible for a matrix to have zero eigenvalues if it is a zero matrix.

How do you prove that a matrix has no multiple eigenvalues?

To prove that a matrix has no multiple eigenvalues, you must show that each eigenvalue has a unique corresponding eigenvector. This can be done by finding the eigenvectors and checking for linear independence.

What is the significance of a matrix having no multiple eigenvalues?

A matrix with no multiple eigenvalues is called a diagonalizable matrix, which means it can be transformed into a diagonal matrix using a similarity transformation. This makes it easier to analyze and perform calculations on the matrix.

How does the condition V=Cx relate to multiple eigenvalues?

The condition V=Cx is equivalent to saying that the eigenvectors of a matrix are linearly independent, which means there are no multiple eigenvalues. This condition is important because it allows us to perform diagonalization and simplifies the analysis of the matrix.

Similar threads

Replies
1
Views
1K
Replies
5
Views
1K
Replies
2
Views
1K
Replies
5
Views
2K
Replies
1
Views
1K
Replies
39
Views
3K
Replies
1
Views
1K
Back
Top